Motor Assessment With the STEGA iPad App to Measure Handwriting in Children.

IMPORTANCE: Handwriting and the fine motor control (hand and fingers) underlying it are key indicators of numerous motor disorders, especially among children. However, current assessment methods are expensive, slow, and subjective, leading to a lack of knowledge about the relationship between handwriting and motor control. OBJECTIVE: To develop and validate the iPad precision drawing app Standardized Tracing Evaluation and Grapheme Assessment (STEGA) to enable rapid quantitative assessment of fine motor control and handwriting. DESIGN: Cross-sectional, single-arm observational study. SETTING: Academic research institution. PARTICIPANTS: Fifty-seven typically developing right-handed children ages 9 to 12 yr with knowledge of cursive. OUTCOMES AND MEASURES: Predicted quality, measured as the correlation between handwriting letter legibility (Evaluation Tool of Children's Handwriting-Cursive [ETCH-C]) and predicted legibility (calculated from STEGA's 120 Hz, nine-variable data). RESULTS: STEGA successfully predicted handwriting (r2 = .437, p < .001) using a support vector regression method. Angular error was the most important aspect of STEGA performance. STEGA was much faster to administer than the ETCH-C (M = 6.7 min, SD = 1.3, versus M = 19.7 min, SD = 5.2). CONCLUSIONS AND RELEVANCE: Assessment of motor control (and especially pen direction control) may provide a meaningful, objective way to assess handwriting. Future studies are needed to validate STEGA with a wider age range, but the initial results indicate that STEGA can provide the first rapid, quantitative, high-resolution, telehealth-capable assessment of the motor control that underpins handwriting. What This Article Adds: The ability to control pen direction may be the most important motor skill for successful handwriting. STEGA may provide the first criterion standard for the fine motor control skills that underpin handwriting, suitable for rehabilitation research and practice.

Performance differences in visually and internally guided continuous manual tracking movements.

Control of familiar visually guided movements involves internal plans as well as visual and other online sensory information, though how visual and internal plans combine for reaching movements remain unclear. Traditional motor sequence learning tasks, such as the serial reaction time task, use stereotyped movements and measure only reaction time. Here, we used a continuous sequential reaching task comprised of naturalistic movements, in order to provide detailed kinematic performance measures. When we embedded pre-learned trajectories (those presumably having an internal plan) within similar but unpredictable movement sequences, participants performed the two kinds of movements with remarkable similarity, and position error alone could not reliably identify the epoch. For such embedded movements, performance during pre-learned sequences showed statistically significant but trivial decreases in measures of kinematic error, compared to performance during novel sequences. However, different sets of kinematic error variables changed significantly between learned and novel sequences for individual participants, suggesting that each participant used distinct motor strategies favoring different kinematic variables during each of the two movement types. Algorithms that incorporated multiple kinematic variables identified transitions between the two movement types well but imperfectly. Hidden Markov model classification differentiated learned and novel movements on single trials based on the above kinematic error variables with 82 +/- 5% accuracy within 244 +/- 696 ms, despite the limited extent of changes in those errors. These results suggest that the motor system can achieve markedly similar performance whether or not an internal plan is present, as only subtle changes arise from any difference between the neural substrates involved in those two conditions.